65 results about "Dataflow computation" patented technology

The invention provides a data processing priority scheduling method for high-speed satellite communication and related equipment, which can effectively improve the transmission performance of a high-speed satellite communication system so as to adapt to differentiated requirements of various communication scenes. The method comprises the following steps: acquiring cross-layer state information; calling a strategy matrix, wherein the strategy matrix is used for defining weight coefficients of different data types under different network states and channel qualities; calculating a target priority score for the to-be-transmitted data stream according to the cross-layer state information and the strategy matrix; sequencing the target priority scores from high to low to obtain a sequencing result; scheduling transmission queues of the data streams for transmission according to a sorting result, and allocating cache transmission queues for the data streams with high priority scores; when it is monitored that the priority score of the new data stream exceeds a preset emergency threshold value, the new data stream is inserted into a cache transmission queue of the transmission queue, and back pressure control is carried out on data streams of other priorities.

The invention relates to the technical field of machine vision, in particular to an aluminum hose quality inspection method and device based on machine vision and computer equipment. The method comprises the following steps: configuring a defect-illumination response matrix, wherein the response matrix comprises detection confidence coefficient values corresponding to multiple preset defect types under multiple illumination conditions; acquiring a historical detection data stream on a current production line, and calculating a prediction probability vector of the multiple preset defect types at the current moment based on the historical detection data stream; inputting the prediction probability vector into an optimization model with a preset constraint condition, and combining the defect-illumination response matrix to solve a target illumination combination which maximizes a weighted detection confidence expectation value; and triggering a light source assembly to work according to the target illumination combination, collecting a surface image of the to-be-detected aluminum hose, and outputting a quality detection result based on the surface image. According to the method, the defects of the aluminum hose can be identified more accurately.

The invention relates to the crossing field of a medical technology and a data processing technology, in particular to a multi-dimensional data fusion ophthalmologic operation safety check data processing system, which comprises a fusion module used for responding to a process release instruction and collecting multiple paths of equipment data streams; calculating a credibility weight based on the multi-path equipment data flow, and performing weighted fusion and time sequence calibration to obtain an intra-operative fusion data set; the calibration module is used for extracting a plurality of preset key risk parameters based on the intraoperative fusion data set to form a multi-dimensional risk feature vector, and performing feature space division and statistical analysis to generate a self-adaptive adjustment coefficient; and using the adaptive adjustment coefficient to calibrate a preset risk determination reference, calculating a comprehensive risk value and outputting a grading risk warning. According to the invention, through fusion of multi-source data, dynamic risk calibration, intelligent response and cross-stage evaluation, a closed-loop data processing flow is constructed, a structured operation safety digital file is generated, and data driving of ophthalmologic operation safety check is realized.

A computing system may receive a data stream from a plurality of data sources of an edge environment. The computing system may identify a confidence score associated with each data stream received from the plurality of data sources. The computing system may, based on the associated confidence score, determine a confidence level of each data stream received from the plurality of data sources. The computing system may select the data streams received from the plurality of data sources having a first specified confidence level. The computing system may perform a data fusion process on the data streams having the first specified confidence level to generate a fused data stream that is an aggregation of the data streams having the first specified confidence level.

This invention discloses a circuit board surface mount correction method, apparatus, system, and medium. The circuit board surface mount correction method includes: acquiring basic accuracy data of the bare circuit board state and solder paste association data after solder paste has been printed on the circuit board; generating a D data stream based on the basic accuracy data, the solder paste association data, and corresponding component information, and sending the D data stream to a pick-and-place machine and a pre-reflow AOI (Automated Optical Inspection) unit; the pick-and-place machine completing component placement based on the D data stream; the pre-reflow AOI acquiring placement result information for each component on the circuit board after placement, and calculating offset data for each component based on the D data stream to generate an H data stream; and the pick-and-place machine performing surface mount correction based on the H data stream. This circuit board surface mount correction method achieves the effect of improving component placement correction accuracy and yield.

The application discloses a kind of bridge construction monitoring data anomaly identification method, system and related equipment, the first data stream of this scheme is filtered, then the statistical characteristic value of several is calculated and extracted to the data stream after filtering, and form a vector based on several statistical characteristic values, form one-dimensional feature vector X;Then the statistical characteristic value of determined feature vector X is selected according to the new feature vector Y of several corresponding characteristic values according to pre-set standard;Then the SVDD model single-class support vector machine is used, the basic classification library is called, and the feature vector Y after dimension reduction is identified, to determine whether it belongs to abnormal data;Finally, the binary classification support vector machine based on SMO algorithm is used, the basic classification library is called, and the data determined as abnormal is judged again.The scheme given by the application can realize high-precision construction monitoring abnormal data identification, improve the early warning efficiency, and can effectively avoid false alarm and false alarm.

The invention discloses a short-term imminent forecast evaluation system and a parallel calculation optimization method, and belongs to the technical field of short-term imminent forecast evaluation. Comprising a data interface module used for receiving a forecast data stream from a forecast model and an observation data stream from an observation network in real time; a dynamic evaluation module which is in communication connection with the data interface module and is used for calculating a group of multi-dimensional evaluation indexes reflecting prediction error space-time distribution characteristics based on the forecast data stream and the observation data stream; the resource decision module is in communication connection with the dynamic evaluation module and is used for receiving the multi-dimensional evaluation indexes and generating a resource scheduling instruction for the next calculation period based on a preset strategy mapping rule; parallel computing clusters; and an optimization scheduling module. According to the short-term and imminent forecast evaluation system and the parallelization calculation optimization method, the problem of resource distribution rigidity caused by disjunction of evaluation and calculation in a traditional system is solved, an intelligent feedback closed loop from evaluation to optimization is constructed, and the overall use effect is good.

The invention provides a millimeter-wave radar boiler heating surface tube crack detection method based on GPU parallelization, and the method comprises the steps: constructing a three-layer parallelization frame integrating data flow, calculation and control, and employing the large-scale parallel calculation capability of a GPU to detect the crack of a heating surface tube of a millimeter-wave radar boiler. The whole-process acceleration processing from preprocessing, range profile reconstruction, feature extraction to intelligent identification is carried out on massive echo signals acquired by the millimeter wave radar, so that high-precision and high-real-time online detection and positioning of the heating surface pipe wall fine cracks are realized in a complex environment in a boiler with strong interference; finally, the detection efficiency is improved, meanwhile, the technical effect of high detection confidence is kept, and powerful technical support is provided for safe operation and preventive maintenance of the power plant boiler.

A method for data transmission in a system includes a) providing at least one piece of data information, b) modulating a carrier signal using the at least one piece of data information to form a modulated signal, c) transmitting the modulated signal, d) receiving the light signal through a camera, which captures an image, sequentially in time, as image rows or image columns, e) determining a first piece of image information and at least one second piece of image information from the image through row-wise or column-wise readings, f) forming a data stream by concatenating the first and the at least one second piece of image information, and g) calculating the signal frequency from the data stream and identifying the at least one piece of data information from the signal frequency.

Methods and systems for optimizing signaling with traffic detection function are described herein. According to an implementation, a computing device, e.g., a session management function (SMF) in a wireless communication network, may receive, from a user plane function (UPF), a first message reporting detection of a first data flow from a user equipment. The computing device may configure a bearer to carry the first data flow based on a traffic flow template (TFT) and a quality of service (QOS) assigned by a policy control function (PCF). In some examples, the computing device may receive, from the UPF, a second message reporting detection of a second data flow from the user equipment. The computing device may update the TFT and use the QoS previously assigned to the bearer to carry the second data flow.

The invention discloses a fluid pulsation accelerator architecture for reinforcement learning of a large language model, and belongs to the technical field of artificial intelligence hardware acceleration. The system aims at solving the problems that when a current universal processor executes an RLHF working load, an instruction driving normal form is not matched with data flow calculation, fixed parallel granularity cannot adapt to a dynamic load, and the efficiency is low due to the fact that a framework does not sense data statistical characteristics. The core of the system is a liquid systolic array calculation fabric capable of being dynamically reconstructed, execution is triggered through data flow, and kernel boundaries and instruction overhead are eliminated. According to the system, a global lookup table subsystem is fused, and nonlinear calculation is optimized through merging query and parallel lookup by using data distribution prior; and a dynamic scheduling unit is configured, and elastic parallelism is realized by adopting a resource allocation algorithm supporting work stealing. According to the method, the throughput and the energy efficiency of RLHF training and a large language model reasoning stage can be remarkably improved, and a new design normal form is provided for a next-generation AI special computing architecture.

A device may include: a bed. A sensor may be configured to configured to: sense pressure applied to the bed; and transmit a pressure stream. A plurality of temperature sensors arranged across the bed can be configured to: sense temperature in a particular location of the bed; and transmit a datastream of the sensed temperature. A computing system comprising can be configured to: receive the pressure stream; receive each of the datastreams; identify an apnea event; generate a sequence of heart rate values for the user for cardiac activity; generate a sequence of skin temperature values for the user; determine a change in heart rate; determine a change in skin temperature within the second time window; determine a relationship between the change in heart rate and the change in skin temperature; and determine a severity score for the apnea event based on the relationship.

The invention relates to the technical field of deep foundation pit engineering safety monitoring, in particular to a deep foundation pit supporting structure stability evaluation and early warning system which comprises a multi-source data and construction event fusion module used for forming a standardized data stream according to acquired multi-source sensor data and construction events; the structure adaptability load quantification module is used for calculating a structure adaptability load index based on the standardized data stream; the steady-state bearing margin attenuation rate evaluation module is used for solving a steady-state bearing margin attenuation rate according to the structural adaptability load index; the trend prediction and grading early warning module is used for generating a grading early warning signal based on the steady-state bearing margin attenuation rate; according to the method, original one-sided and isolated monitoring indexes are converted into accurate quantification of structure accumulation and comprehensive loads, and the defect that evaluation is one-sided in a traditional method is overcome.

The present invention relates to an adaptive and reconfigurable dataflow computing system and method. The dataflow computing includes a dataflow-driven core to dynamically reprogram itself based on computational needs during runtime. In this system, an application program is broken down into a dataflow graph with functional units by a compiler, and an instruction dispatcher allocates and runs available processing elements to execute all functional units in the dataflow graph to perform distributed or parallel processing. The dynamic allocation of processing units can be adapted during runtime.

The invention relates to a method for detecting anomalies in a data stream. The method including a training step comprising, based on at least one reference data stream, computing a value of at least one predetermined non-conformity feature, thereby obtaining a conformity index; for each reference data stream, computing a reference behavioral dataset including, for at least one predetermined behavioral feature, a value of said behavioral feature computed based on said reference data stream, The training step also includes, for each reference data stream, generating a respective reference augmented behavioral dataset including the respective reference behavioral dataset and, for each non-conformity feature, a respective deviation value equal to zero; and training an artificial intelligence model based on each reference augmented behavioral dataset, each reference augmented behavioral dataset being associated with information indicative of the absence of anomaly in the corresponding reference data stream.

The invention discloses an electric tool full life cycle management system based on digital twinning, and belongs to the technical field of digital twinning and intelligent equipment management in the industrial manufacturing field, and the system comprises a data collection module which is used for obtaining a multi-dimensional data flow from an electric tool physical entity and an operation environment in real time; the multi-dimensional data flow comprises motor real-time current, real-time environment temperature and machining material hardness; the state evaluation and life prediction module is used for calculating a real-time degradation rate based on the multi-dimensional data stream and generating a residual effective life based on the real-time degradation rate; the real-time degradation rate reflects the thermal-mechanical coupling effect; the control strategy generation module is used for generating a self-adaptive control instruction according to the remaining effective life and in combination with a preset operation target decision rule set; the self-adaptive control instruction is used for regulating and controlling the peak value output torque of the electric tool, a factual basis is provided for achieving accurate service life prediction and performance regulation and control, and the management fineness and accuracy are improved.

A computer collects data relevant to a primary activity occurring in a data gathering zone. The computer receives from an Activity Indication Source (AIS), an indication of a primary activity associated with a Data Gathering Zone (DGZ). The computer determines, uses a first Machine Learning (ML) model to identify data streams relevant for data gathering activities, a Target Set of Data Streams (TSDS) associated with the primary activity. The computer identifies within a set of sensors having a monitoring arrangement with the DGZ, a set of preferred sensors to provide the TSDS. The computer determines using a second Machine Learning (ML) model trained to identify Preferred Data Stream Attributes (PDSAs) for data gathering activities, a target set of PDSAs. The computer retrieves, a data stream relevant to the TSDS. The computer compares attributes of the data stream to the PDSAs and initiates corrective action as necessary.

The invention relates to the technical field of big data analysis, in particular to a diet image feature-based personalized recipe database retrieval method, which comprises the following steps of: setting a time sliding sampling window according to a user diet image log; according to the method, the time sliding sampling window is set based on the user diet image log, the greasiness and sweetness characteristic values are extracted, the dynamic taste change trend of the user in a specific time period can be captured, the defect that a static tag is difficult to reflect real-time physiological preferences is overcome, and the user experience is improved by combining the generated dynamic taste preference vector. A higher weight is effectively given to recent dietary behaviors, sensitive perception of the current appetite state of a user is realized, a data stream is traversed and clicked to calculate the difference of adjacent sampling sections, and second-order difference operation is performed to generate a popularity trend acceleration value, so that popularity hysteresis caused by a single accumulated click rate is broken through, the rising or falling rate of recipe popularity is identified, and the popularity of the recipe is improved. Therefore, related contents with explosion potential can be mined.

Method for configuring the TAS protocol of a TSN network and estimating the worst-case end-to-end latencies of transmitted data streams and associated devices. The present invention relates to a method for estimating the worst-case end-to-end transmission latency of frames in a TSN communication network operating according to a TAS protocol, the method comprising: - for each data stream, the calculation of the worst-case end-to-end transmission latency value that a frame of the data stream may experience, the calculation step comprising, for each frame of the data stream and each output port of a node or switch through which a data stream may pass, the operations for estimating the worst-case disturbance for the transmission of the frame on the considered output port from a plurality of contributions.and - a step estimating the worst-case end-to-end transmission latency of a TSN network from the contributions for each data stream. Figure for the abbreviation: Figure 1,

The application relates to the technical field of data processing, and discloses a social cognitive evaluation method and system based on attention, a terminal and a readable storage medium. The method comprises the following steps: guiding a user to make a plurality of social cognitive selections, collecting video data streams and selection data in each social cognitive selection process; calculating a systematic index and an attention index according to the selection data and the video data streams; and generating an evaluation result of social cognitive according to the systematic index and the attention index. Through the collection of the video of the user in each cognitive selection process, analysis is carried out according to the video, whether the attention of the user is concentrated in the place where the attention should be concentrated when the selection is made is judged, the attention condition of the user in the cognitive selection process is analyzed, and the social cognitive ability of the user is judged by using the selection data.

The invention relates to a newborn nursing skill training method, system and device based on a virtual reality technology and a medium. The method comprises the following steps: based on a newborn physiological digital twinborn basic model set, performing parameter identification generation on newborn personalized indexes to obtain a personalized newborn digital twinborn model; based on a scene library, according to the current node of the training path, generating a VR scene in combination with a personalized neonatal digital twin model; vR tracking equipment action data flow and real-time physiological data flow are acquired, and a newborn physiological response state is calculated based on the personalized newborn digital twinborn model; reconstructing audio-visual feedback corresponding to the VR scene according to the physiological response state of the newborn; and in response to an end mark of the scene time window, calculating an evaluation index according to the action data flow and the real-time physiological data flow of the VR tracking equipment, and obtaining a training result report. By adopting the method, the practice times and scene coverage can be amplified in a virtual environment, and the dependence on a real scene is reduced.

This invention relates to the fields of big data analysis and artificial intelligence technology, and discloses a method for dynamic analysis of farmland quality based on multi-source sensor fusion. The method includes: acquiring multi-source distributed input branch data streams characterizing the operating state of a controlled system; calculating the mutual information entropy increment between each sampled branch and the global feature evolution trend; extracting a set of high-confidence branches through logical arbitration; constructing a local tangent space of the feature manifold using local tangent plane basis vectors; mapping the projection deviation vector of the damaged branch to this local tangent space and solving for the minimum value of the projection energy functional; and achieving logical reconstruction of the missing dimensions. This invention utilizes a manifold consistency reconstruction mechanism to eliminate the contradiction of topological drift from heterogeneous information sources, effectively improving the logical robustness of the analysis results of the controlled system under complex operating conditions.

The grid safety supervision system and safety supervision method of the present application belong to the technical field of power system safety monitoring and intelligent early warning, and comprise the following steps: S1, collecting and pre-processing multi-source heterogeneous monitoring data of the power grid to generate standardized data flow; S2, based on the standardized data flow, calculating the electromagnetic transient harmonic fingerprint fractal dimension, constructing the node-level safety event correlation degree tensor, calculating the whole-network safety situation topology entropy, and calculating the decision tree fractal complexity; S3, combining the electromagnetic transient harmonic fingerprint fractal dimension, the whole-network safety situation topology entropy and the decision tree fractal complexity, calculating the instantaneous risk factor; S4, based on the instantaneous risk factor and the preset safety threat propagation time lag kernel function, generating the normalized risk index through the normalized convolution operation, which overcomes the defects of the prior art that relies on isolated indicators and fixed thresholds and cannot identify potential threats early.

The application provides a system, platform and method for data integration based on real-time data flow, the system comprising a collector configured to generate a real-time data flow in response to a first data source and collect the real-time data flow; wherein the first data source is one of a plurality of data sources, and the real-time data flow is generated based on user operation; a data flow processor configured to calculate a real-time score of the first data source based on real-time data of each evaluation index extracted from the real-time data flow; obtain a historical score calculated based on historical data flow of a second data source, and calculate a real-time user experience evaluation score based on the real-time score and the historical score; and a data flow outputter configured to output the user experience evaluation score in different output formats according to different output positions. The application can be used to trigger the calculation and analysis of the user experience evaluation score immediately after submitting feedback or performing business operations, thereby improving response speed and user experience.

Methods and systems for optimizing signaling with traffic detection function are described herein. According to an implementation, a computing device, e.g., a session management function (SMF) in a wireless communication network, may receive, from a user plane function (UPF), a first message reporting detection of a first data flow from a user equipment. The computing device may configure a bearer to carry the first data flow based on a traffic flow template (TFT) and a quality of service (QoS) assigned by a policy control function (PCF). In some examples, the computing device may receive, from the UPF, a second message reporting detection of a second data flow from the user equipment. The computing device may update the TFT and use the QoS previously assigned to the bearer to carry the second data flow.

The invention discloses a pharmaceutical process adaptive regulation and control method and system based on information fusion, and the method comprises the steps: obtaining a multi-parameter real-time data stream, and obtaining a standardized multi-parameter data set; classifying the standardized multi-parameter data set to generate an abnormal coupling parameter pair set; constructing a high-dimensional data interaction map, and generating a correlation intensity matrix; obtaining probability distribution of by-product generation risks in a future preset time, and outputting a risk early warning signal; obtaining an optimized risk early warning signal; after obtaining a dominant parameter configuration list, activating an adaptive control system, and obtaining optimized dominant parameter configuration; and generating a verification data stream, calculating a deviation degree between the first data set and the standardized multi-parameter data set, and generating a log file. According to the method, through multi-parameter dynamic coupling analysis and self-adaptive feedback regulation and control, the stability of the reaction process is remarkably improved, the risk of byproducts is reduced, the log file is generated, iterative optimization is supported, and accurate and efficient pharmaceutical process control is achieved.

A method of identifying a defensive alignment and an offensive alignment in a set-piece is disclosed herein. A computing system receives one or more streams of tracking data. The computing system identifies a set-piece contained in the one or more streams of tracking data. The computing system identifies a defensive alignment of a first team and an offensive alignment of a second team. The computing system extracts, via a convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing the set-piece through the convolutional neural network. The computing system scans the set-piece, via a machine learning algorithm, to identify one or more features indicative of a type of offensive alignment implemented by the second team. The computing system infers the type of defensive alignment implemented by the first team.

A bed has a mattress. A plurality of temperature sensors are each temperature sensor configured to: sense temperature in a particular location of the bed; and transmit, to a computer system, a datastream of the sensed temperature. A computing system is configured to: receive each of the datastreams of the sensed temperature; select a subset of the datastreams as selected datastreams; aggregate the selected datastreams into time-bound windows of temperature data; select a subset of the time-bound windows of temperature as selected temperature values; access a machine-learning temperature classifier; provide, to the machine-learning temperature classifier, the selected temperature values; and receive, from the machine-learning temperature classifier, the distal wrist temperature for the user.

The application discloses a short-term weather forecast evaluation system and a parallel computing optimization method, and belongs to the technical field of short-term weather forecast evaluation.The short-term weather forecast evaluation system comprises a data interface module, a dynamic evaluation module, a resource decision module, a parallel computing cluster and an optimization scheduling module.The data interface module is used for receiving real-time forecast data streams from a forecast model and observation data streams from an observation network.The dynamic evaluation module is in communication connection with the data interface module and is used for calculating a plurality of multi-dimensional evaluation indexes reflecting the spatio-temporal distribution characteristics of prediction errors based on the forecast data streams and the observation data streams.The resource decision module is in communication connection with the dynamic evaluation module, is used for receiving the multi-dimensional evaluation indexes, and generates resource scheduling instructions for a next calculation period based on a preset strategy mapping rule.The parallel computing cluster is used for performing parallel computing on the resource scheduling instructions.The optimization scheduling module is used for optimizing the resource scheduling instructions.The short-term weather forecast evaluation system and the parallel computing optimization method not only solve the resource allocation rigidity problem caused by the disconnection between evaluation and calculation in a traditional system, but also construct an intelligent feedback closed loop from evaluation to optimization, and the overall use effect is good.

A computing system may receive a data stream from a data source of an edge environment. The computing system may identify at least one of a first confidence score or first trust metadata associated with the data stream received from data source. The computing system may apply a caching algorithm to the data stream, the caching algorithm implementing one or more caching parameters that specify criteria to be used to determine if the data stream is to be placed in a data cache. The computing system may, based on the results of applying the caching algorithm and the one of the first confidence score or first trust metadata, determine if the data stream is to be placed in the data cache.